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update_salary

Update your salary/compensation details. IMPORTANT: For INR (currency code 2), pass values in lakhs (e.g., 20 for 20 lakhs). For all other currencies (USD, etc.), pass values in thousands (e.g., 400 for $400K). Use get_currencies to look up currency codes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
baseNoBase salary. For INR: in lakhs (e.g., 20 = 20 lakhs). For USD/others: in thousands (e.g., 400 = $400K)
bonusNoAnnual bonus. Same unit convention as base
stocksNoAnnual stock/equity value. Same unit convention as base
currencyNoCurrency code. Use get_currencies to look up valid codes (e.g., 2 = INR, 3 = USD)
signingBonusNoSigning bonus. Same unit convention as base
targetSalaryNoTarget salary. Same unit convention as base

TDQS

A3.8/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden of behavioral disclosure. It explains the unit convention, but does not disclose whether the update overwrites all fields, supports partial updates, requires specific permissions, or is reversible. For a mutation tool, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact, with the key action front-loaded. The 'IMPORTANT' flag draws attention to the critical unit convention. The second sentence is a bit dense but not excessive, and every sentence adds essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description explains the main operational quirk (units) but does not address partial updates, whether all fields are optional (suggested by no required params), or what happens on success/failure. It is adequate for a simple update tool but leaves room for more context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by summarizing the unit convention with concrete examples ('20 for 20 lakhs', '400 for $400K') and explicitly pointing to get_currencies, which goes beyond the per-parameter schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Update') and the resource ('your salary/compensation details'), with a specific scope. It is unambiguous and distinct from sibling tools like get_salary or get_currencies.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear usage context, especially the critical unit conventions for INR vs. other currencies, and explicitly instructs to use get_currencies for looking up codes. It does not explicitly state when not to use the tool, but the guidance is strong enough to direct correct usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.7/5.0
Disambiguation5/5

Each tool targets a distinct resource and action. Tools like get_job vs get_application vs get_job_hunt are clearly separated, and match_jobs vs search_jobs are well-differentiated by saved vs explicit filters. No two tools appear to do the same thing.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., create_job_hunt, list_applications, update_salary). Even longer names like add_job_to_applications maintain the convention with clear, predictable structure.

Tool Count2/5

With 35 tools, the server exceeds the 25+ threshold that indicates an overly large surface. While the breadth covers a comprehensive job search workflow, the number is likely overwhelming and could be consolidated without losing functionality.

Completeness5/5

The tool set covers the full job hunt lifecycle: creating hunts, searching/matching jobs, applying, tracking applications, managing resumes (including AI-generated versions), outreach, interviews, profile, and compensation. There are no obvious dead ends; update and delete operations are available where needed.

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